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AI Data Engineer Langchain | AWS | SQL | Onsite (Quad Cities)

Macpower Digital Assets Edge Private Limited

  • Bettendorf, IA
  • 3 days ago
  • $60–$65 Per Hour

Highlights

Position Summary: We are seeking a highly skilled and motivated AI Data Engineer with hands-on experience in Langchain , large data sets , AWS , and deep expertise in SQL and databases . In this role, you'll design, develop, and maintain scalable data pipelines, enable AI model integration, and manage large-scale datasets in cloud environments.

Numbers & Facts

LocationBettendorf, IA
Salary$60–$65 Per Hour

Description

NOTE:
  • W2 only no C2C, please.
  • This position is onsite in the Quad Cities (IA/IL) area at our client's facility.
  • No sponsorship is available now or in the future for this role. Candidates who require or will require sponsorship will not be considered.
  • Candidates MUST appear on video at every stage of the interview process. If a candidate cannot be on camera, they will not be considered.
  • Hourly pay is based on experience and W2 ONLY
Position Summary:
  • We are seeking a highly skilled and motivated AI Data Engineer with hands-on experience in Langchain, large data sets, AWS, and deep expertise in SQL and databases. In this role, you'll design, develop, and maintain scalable data pipelines, enable AI model integration, and manage large-scale datasets in cloud environments.
  • Our client is a leader in AI and data innovation, empowering organizations to drive growth and efficiency through cutting-edge technology. As part of their team, you will help build robust AI-driven systems powered by large-scale data solutions.
Key Responsibilities:
  • Data Engineering: Design, develop, and manage efficient data pipelines for large datasets, with a focus on scalability and performance.
  • Langchain Integration: Leverage Langchain to automate workflows, enhance AI models, and streamline data-driven processes.
  • Cloud Infrastructure (AWS): Build and scale cloud data environments using AWS services such as S3, EC2, Lambda, Redshift, and more.
  • SQL & Databases: Write advanced SQL queries for data extraction, transformation, and analysis. Ensure database performance and integrity.
  • Collaboration: Partner with Data Scientists, AI Engineers, and cross-functional teams to ensure data readiness for machine learning and analytics use cases.
  • Data Quality & Governance: Monitor and maintain data quality, implement error-handling procedures, and ensure adherence to privacy and compliance standards.
  • Performance Optimization: Continuously improve and fine-tune data pipelines and queries for better performance and scalability.
  • Documentation: Create and maintain thorough documentation of data architecture, workflows, and processes for knowledge sharing and collaboration.
Experience:
  • 3+ years as a Data Engineer, with a focus on AI or machine learning pipelines
  • Proven experience using Langchain to develop AI workflows and automation
  • Strong experience with large-scale data and distributed systems
  • Proficiency in SQL and hands-on experience with both relational (e.g., PostgreSQL, MySQL, SQL Server) and NoSQL databases
  • Deep familiarity with AWS services such as S3, EC2, Lambda, Redshift, and RDS
Technical Skills:
  • Proficient in Python
  • Solid understanding of database design, data modeling, and query optimization
  • Familiar with data warehousing concepts and tools
  • Experience with data pipeline orchestration tools like Apache Airflow (or similar)
  • Strong knowledge of AI/ML data workflows, including preprocessing and feature engineering
Education:
  • Bachelor's or Master's degree in Computer Science, Data Engineering, Artificial Intelligence, or a related field
Soft Skills:
  • Excellent analytical and problem-solving abilities
  • Strong communication skills, especially in explaining complex concepts to non-technical audiences
  • Ability to manage multiple priorities in a fast-paced environment
  • Collaborative and team-oriented work ethic
Preferred Qualifications
  • Experience with Apache Spark, Kafka, or other big data tools
  • Familiarity with Docker and Kubernetes for deploying scalable data solutions
  • Knowledge of AI-specific workflows, such as data preparation for natural language processing (NLP), computer vision, or other AI domains

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